Recommender systems form a specific type of information filtering technique that attempts to present media items (e.g., movies and/or television programs, etc.) that are likely of interest to a user. A recommender system typically compares a user's profile to some reference characteristics, and seeks to predict the “rating” that a user would give to a media item the user had not yet considered.
A recommender system typically uses a content-based approach or a collaborative filtering approach. In a content-based approach, the recommender system compares the user's profile to content from the media item of interest. In a collaborative filtering approach, the recommender system compares the user's profile to profiles of other users. For example, a recommender system may collect data by using collaborative filtering systems to determine a user's tastes and interests as the user searches the Internet. Sites may gather information about the user's personal interests, compare the user's information to other information from users with similar interests and make recommendations (e.g., movies the user will likely enjoy, a book the user should read, etc.).